||<p>Abstract</p><p>Intensity Modulated Radiation Therapy (IMRT) and Volumetric Modulated
Arc Therapy (VMAT) have become effective tools for treating cancer with radiation.
Designing a high quality IMRT/VMAT treatment plan is time consuming. Different kinds
of knowledge-based methods are being developed to reduce planning time and improve
the plan quality by extracting knowledge from previous expert plans to form knowledge
models and applying such models to the new patient cases. Currently, these methods
are mostly limited to a particular cancer type and therefore various diseases types
require training of multiple knowledge models with a large number of cases. </p><p>To
investigate the feasibility of knowledge modeling of IMRT/VMAT treatment planning
for multiple cancer types, a progressive study is conducted with a treatment planning
knowledge model that quantifies correlations between patient pelvic anatomical features
and the OAR sparing features. Low risk prostate plans with relatively simpler PTV-OAR
geometry, which is the most common geometry type in previous knowledge based studies,
are used to train the model as the starting point of the progressive modeling process.
Cases with more complex PTV-OAR anatomies (prostate cancer cases with lymph node irradiation,
and anal rectal cancer cases) are added to the training dataset one by one until the
model prediction accuracies reach plateau. The DVHs predicted by the knowledge model
for bladder, femoral heads and rectum are validated by cases from all three types
of cases. Dosimetric parameters are extracted from the predicted DVHs and the corresponding
actual plan values measure the prediction accuracy of this multi-disease type model.
Further, its accuracy was also compared with the models trained by single disease
type cases (including low risk prostate cancer, or type 1, high risk prostate cancer
with lymph nodes, or type 2 and anal rectal cancer, or type 3). </p><p>Prediction
accuracy reaches plateau when 6 high risk prostate cancer with lymph nodes irradiation
cases and 8 anal rectal cancer cases were added to the training dataset. The determination
coefficients R2 for the OARs are: Bladder: 0.90, rectum: 0.64 and femoral heads: 0.82.
The prediction accuracies by the multi-disease type model and single-disease type
models have no significant differences by F-test (p-value: bladder: 0.58, femoral
head: 0.44, rectum: 0.97). </p><p>Conclusion:</p><p>Progressive knowledge modeling
of OAR sparing for multiple cancer types in in the pelvic region is feasible and has
comparable accuracy to single-disease type modeling.</p>